• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.483-489
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• 2009

Electrolytic capacitors are used in variety of equipments as smoothening element of the power converters because it has high capacitance for its size and low price. Electrolytic capacitors, which is most of the time affected by aging effect, plays a very important role for the power electronics system quality and reliability. Therefore it is important to estimate the parameter of an electrolytic capacitor to predict the failure. This objective of this paper is to propose a new method to detect the rise of equivalent series resistor(ESR) in order to realize the online failure prediction of electrolytic capacitor for DC output filter of power converter. The ESR of electrolytic capacitor estimated from RMS result of filtered waveform(BPF) of the ripple capacitor voltage/current. Therefore, the preposed online failure prediction method has the merits of easy ESR computation and circuit simplicity. Simulation and experimental results are shown to verify the performance of the proposed on-line method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.40-46
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• 2010

DC link electrolytic capacitors are widely used in various PWM power converter system, such as adjustable speed driver(ASD) or DC/DC converter. Electrolytic capacitors, which is the most of the time affected by aging effect, plays a very important role for the power electronics system quality and reliability. This objective of this paper is to propose a improvement method to detect the rise of equivalent series resistor(ESR) in order to realize the online failure prediction of electrolytic capacitor for DC link of PWM power converter. The ESR detection scheme is based on the determination of the electrolytic capacitor AC losses calculated from voltage/current measurement using AC coupling. Therefore, the preposed online failure prediction method has the merits of easy ESR computation and circuit simplicity compare with BPF method. Simulation results show the veridity of the proposed on-line ESR estimation method.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• IE interfaces
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• v.25 no.4
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• pp.376-381
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• 2012

As the number, complexity and interaction of electrical, electronic and programmable electronic (E/E/PE) systems increase, a growing emphasis has been placed on the concept of functional safety during product development. IEC 61508 provides guidelines and standardized procedures in the development of reliable and dependable E/E/PE systems to assure functional safety. Determining risk classes (i.e., safety integrity levels, SILs) associated to a specific E/E/PE item may be recognized as one of the most crucial activities in the product development per IEC 61508 since SILs are used to specify necessary safety requirements for achieving an acceptable residual risk. This article presents a case study on the assessment of SILs applying failure modes, effects and diagnostic analysis (FMEDA) from which failure rates may be derived for each important failure category by combining a standard FMEA with online diagnostic techniques.

• Korean small business review
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• v.42 no.3
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• pp.43-75
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• 2020

The main purpose of this study is to analyze research trend of 'business failure' from academic papers published in Asia Pacific Journal of Small Business. In this review, first, we reviewed research trend of failure, published in academic journals at abroad, explored the major topics, and set forth the framework of classification. Second, we selected and analyzed 16 Korean articles in a refined search from total 1,060 articles published in Asia Pacific Journal of Small Business from 1979 to 2019. Third, in order to understand overall research trend in Korea, additional publication search was done by online database system using keywords, and 24 other articles were selected. As a result, five research themes were identified and analyzed: (1) bankruptcy prediction, (2) emotion before and after failure, (3) costs of failure, (4) causes of failure, and (5) reentry determinants. We believe that this purposed review will offer future research issues regarding business failure.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.881-885
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• 2013

In this paper, we present a deterioration judgment model of urban rail power equipment using driving history, the frequency and number of failures. In addition, we have developed a deterioration judgment program based on the derived failure rate. A deterioration judgment model of power equipments on metro system was designed to establish how much environmental factors, such as thermal cycling, humidity, overvoltage and partial discharge. The deterioration rate of the transformers followed the Arrhenius log life versus reciprocal Kelvin temperature (hotspot temperature) relation. The deterioration judgment program is linked to the online condition monitoring system of urban railway system. The deterioration judgment program is based on the user interface it is possible to apply immediately to the urban rail power equipment.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.